Document Detail


Phase-locking factor in a motor imagery Brain-Computer Interface.
MedLine Citation:
PMID:  23366525     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
A Brain-Computer Interface (BCI) attempts to create a direct channel of communication between the brain and a computer. This is especially important for patients that are "locked in", as they have limited motor function and thus require an alternative means of communication. In this scope, a BCI can be controlled through the imagination of motor tasks, i.e. Motor Imagery. This thinking of actions produce changes on the ongoing Electroencephalogram (EEG), such as the so called Event-Related Desynchronization (ERD), that can be detected and measured. Traditionally, ERD is measured through the estimation of EEG signal power in specific frequency bands. In this work, a new method based on the phase information from the EEG channels, through the Phase-Locking Factor (PLF), is proposed. Both feature types were tested in real data obtained from 6 voluntary subjects, who performed 7 motor tasks in an EEG session. The features were classified using Support Vector Machine (SVM) classifiers organized in a hierarchical structure. The results show that the PLF features are better, with an average accuracy of ≈ 86%, against an accuracy of ≈ 70% for the band power features. Although more research is still needed, the PLF measure shows promising results for use in a BCI system.
Authors:
Carlos Carreiras; Luis Borges de Almeida; J Miguel Sanches
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2012     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-31     Completed Date:  2013-08-26     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2877-80     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Brain-Computer Interfaces*
Electroencephalography
Humans
Support Vector Machines

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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